On-Orbit Radiometric Calibration Accuracy of FY-3A MERSI Thermal Infrared Channel
XU Na1, 2, HU Xiu-qing1, 2*, CHEN Lin1, 2, ZHANG Yong1, 2 , HU Ju-yang1, 2, SUN Ling1, 2
1. National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China 2. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081, China
Abstract：Accurate satellite radiance measurements are significant for data assimilations and quantitative retrieval applications. In the present paper, radiometric calibration accuracy of FungYun-3A (FY-3A) Medium Resolution Spectral Imager (MERSI) thermal infrared (TIR) channel was evaluated based on simultaneous nadir observation (SNO) intercalibration method. Hyperspectral and high-quality measurements of METOP-A/IASI were used as reference. Assessment uncertainty from intercalibration method was also investigated by examining the relation between BT bias against four main collocation factors, i.e. observation time difference, view geometric difference related to zenith angles and azimuth angles, and scene spatial homogeneity. It was indicated that the BT bias is evenly distributed across the collocation variables with no significant linear relationship in MERSI IR channel. Among the four collocation factors, the scene spatial homogeneity may be the most important factor with the uncertainty less than 2% of BT bias. Statistical analysis of monitoring biases during one and a half years indicates that the brightness temperature measured by MERSI is much warmer than that of IASI. The annual mean bias (MERSI-IASI) in 2012 is (3.18±0.34)K. Monthly averaged BT biases show a little seasonal variation character, and fluctuation range is less than 0.8K. To further verify the reliability, our evaluation result was also compared with the synchronous experiment results at Dunhuang and Qinghai Lake sites, which showed excellent agreement. Preliminary analysis indicates that there are two reasons leading to the warm bias. One is the overestimation of blackbody emissivity, and the other is probably the incorrect spectral respond function which has shifted to window spectral. Considering the variation character of BT biases, SRF error seems to be the dominant factor.
[1] YANG Jun, DONG Chao-hua, LU Nai-meng, et al(杨 军, 董超华, 卢乃锰，等). Meteorological Sinica(气象学报), 2009, 67(4): 501. [2] Goldberg M D. Proc. SPIE, Atmospheric and Environmental Remote Sensing Data Processing and Utilization Ⅲ: Readiness for GEOSS, 2007, 6684: 668402. [3] Chander G. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1056. [4] Gunshor Mathew M, Timothy J Schmit, W Paul Menzel, et al. J. Atmos. Oceanic Technol., 2009, 26: 746. [5] Hewison T J, Wu X, Yu F, et al. IEEE Trans. Geosci. Remote Sens., 2013，51(3): 1160. [6] Xu Na, Hu Xiu-qing, Chen Lin, et al. Journal of Remote Sensing, 2012, 16(5): 939. [7] Zhang Y, Gunshor M M. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1231. [8] Hu Xiu-qing, Xu Na, Weng Fu-zhong, et al. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(10): 5008. [9] Wang L. Journal of Atmospheric and Oceanic Technology, 2009, 26: 1843. [10] Yu F, Wu X. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1187. [11] Hewison T J. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1171. [12] Hewison T J, Muller J. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1182. [13] Yoshihiko T. Meteorological Satellite Center Technical Note, 2008, 50: 1. [14] Aumann H H, Broberg S, Elliott D, et al. J. Geophys. Res., 2006, 111, doi: 10.1029/2005JD006822. [15] Blumstein D, Tournier B, Cayla F R, et al. Proc. SPIE, Atmospheric and Environment Remote Sensing Data Processing and Utilization III: Readiness for GEOSS, 2007, 6684: 66840H. [16] Wang L, Goldberg M, Wu X, et al. J. Geophys. Res., 2011, 116: D11111. [17] HU Xiu-qing, ZHANG Li-yang, ZHENG Zhao-jun, et al(胡秀清，张里阳，郑照君，等). Optical and Precision Engineering(光学精密工程), 2010, 18(9): 1972.
